If you’re not making mistakes, then you’re not doing anything. I’m positive that a doer makes mistakes. John Wooden

One of the key lessons in life is what to do when you make a mistake. They key is not to get frustrated, not to give up, but instead to use those mistakes to learn and grow and get better. Another key lesson is not to settle for good enough and to always keep looking for things to correct.

And avoid the disease of more.

As you know if you follow this space, we’ve been working through the prospects in the upcoming NBA draft in a variety of ways. For me, this means my 4th annual draft rankings using my statistically-based model. I posted a first revision but it had some key mistakes but I was able to quickly correct course and get a nice and complete revision two up.

It wasn’t quite enough. Some of you sharp eyed readers noticed that there was something slightly off with the model.

The overall distribution by position was off. By it’s construction, the model was not being aggresive enough when selecting centers and it was also not casting a wide enough net. I had been planning to address these issues in the offseason but I finally decided to breakdown and correct these flaws as best as possible. The corrections were as follows:

I tweaked the age model to more accurately reflect the reality of players going into the league.

I changed how I do my baseline by position from using the NBA population to the actual historical population of incoming draftees. Functionally, I assume that the average Raw productivity of an average NBA player is one standard deviation above the mean productivity for a drafted College player.

I did a few additional minor tweaks as well and the end result looks like so:

The new model has a more even distribution and more importantly makes more picks. The big question is how did that impact accuracy?

While there is some loss in accuracy, it corrects some other problems and is still better than a lottery pick.

That means we are good to go.

At least until the offseason were I go for accuracy and increased range! Science! (image courtesy of http://xkcd.com)

Welcome then to the third and final revision of my fourth annual draft preview and rankings, where I take it upon myself to write, project, and speculate about the NBA draft using a surprisingly effective draft model to predict player performance using data publicly available on the internet.

Third time’s the charm

The original build in detail is achived here (parts 1 & part 2 ). In very general terms, the models use the available data to predict future performance for each player coming into the draft from the NCAA. Based on that prediction, a ranking is done and a draft recommendation is generated.

It has performed at a very high level. For the full history you can go to:

A full review (here) for a full breakdown and here for the 2011 version).

A full review of the new version will be coming, say it with me, in the offseason.

Now for the 2013 NBA Draft Rank version number 3. I’ve included all eligible NCAA Prospect in Draft Express Top 100 and all recommended draftees by the model outside of the Draft Express Top 100 (this includes some interesting names). First, the table sorted by the draft express rankings:

Now, let’s sort that by projected productivity:

We are now officially at over 100 hours of prep work for this draft. Again, that’s the productivity projection for every eligible NCAA draft prospect who made it into Draft Express‘ Top 100 or was identified by the model as a possible NBA player. As always, my plan is to continue to monitor these projections in the future.

Let’s focus down on the draft targets:

We now have eighteen prospects that the model insists should be drafted that probably will be (up from ten). With ten blue chippers (rated as sure fire and recommended by both models) in the group. Let’s do updated takeaways shall we?

You’ll note that Mr. Noel now slides comfortably into the #1 overall slot and Mike Muscala (#4) joins him on the list of blue chip centers.

All these players are really perfect for the NBA GM looking to fill out your D-League team. The model looks at them and sees a particular outstanding skill (i.e. Mike Hart and rebounding, DJ Stephens and ridiculous athleticism). I also really would not be surprised to see them doing well next season in Europe (or with the Austin Toros).

Let’s make sure to cover the potential landmines in round 1 as well:

Cavaliers picking Alex Len would be objective proof that God hates Cleveland or just the Comic Sans font. I mean, there is really only one possibly devastating pick in the first ten and it might just go to Cleveland. Unreal.

That 10 to 20 group is a minefield. I really don’t want Zeller, Shabazz, Dieng, or Plumlee on the Celtics. Faced with that choice, I hope to god Danny Ainge goes to Rudy Gobert.

We still of course need to talk about Europe.

That’s the available data for all European Prospects (for whom I have data). If I throw this data into my Euro model (shown here) I get:

This now means I have data and a projection for pretty much everyone in the draft. That means I can build a cheat sheet as a graph:

Today, you’ll also get a mock draft and quite possibly live blogging during the draft. Never let it be said that we are not a full service outfit.

-Arturo

P.S. I wanted to answer some questions/Comments:

dynamo.joe says:

lol, in 3 revs this went from a shitty draft, to a good draft, to the greatest draft in the modern history of the NBA. It does look a lot more like I was expecting to see, so I guess my intuition is happy.

Hmmm:

Yeah, it actually is. Who knew? :-)

mindB says:

Is there any way we could get the hit rate on the difference between this model and the last since ’96?

lol, in 3 revs this went from a shitty draft, to a good draft, to the greatest draft in the modern history of the NBA. It does look a lot more like I was expecting to see, so I guess my intuition is happy.

I guess the exception there is I was expecting to see more Northwestern State players. There is something going on in northern Louisiana.

It’s alright Cleveland fans, I would bet just about anything that they don’t take Len barring a massive turnaround in philosophy. For the same reason I was convinced and in bet anything mode that they were passing on Barnes last year. The Cavaliers are at the forefront of using advanced metrics to draft players. Not WP, but virtually all advanced metrics treat steals as massively important and Len’s all time bad steal rate probably puts them off the table for them. Especially considering we know the Cavaliers treat steals as important as evidenced by their Dion Waiters pick last year, the big thing Waiters had going for him advanced metrics wise was that massive steal rate. Then there’s the fact that Noel is one of the most dominant advanced metrics performers we’ve seen, so the idea of the Cavs with their statguy history taking Len over Noel, is nearly unthinkable. Tristan Thompson’s big thing stat wise was his block rate rate, Waiters’ was his steal, Noel is like Thompson + Waiters statistically in those categories. Even if they hate Noel’s knees, Oladipo and Porter are also statguy favorites and the likely consolation prize.